Search

A 64-year-old man with a history of diabetes and gallstones was admitted to our institution with suspected pancreatic malignancy. Computed tomography (CT) revealed multiple pancreatic cysts and massive ascites, and endoscopic ultrasonography (EUS) revealed a 28×27-mm hypoechoic mass in the pancreatic head. An EUS-guided fine-needle aspiration biopsy was performed, and there were no malignant findings. Based on the test results and imaging findings, type 1 autoimmune pancreatitis was suspected. The patient was administered 30 mg of prednisolone daily. After 11 days, CT revealed that the pancreatic cysts and ascites had reduced in size.

Radiation-induced hemorrhagic gastritis is a relatively uncommon complication of irradiation that can be severe. However, appropriate treatment guidelines have not yet been established because of the small number of known cases. At our hospital, we encountered nine cases of radiation-induced hemorrhagic gastritis between July 2005 and July 2018. All patients initially underwent argon plasma coagulation (APC) for hemostasis. The treatment was highly effective, and hemostasis was successfully achieved in eight of the cases. Hemostasis could not be achieved in one case treated with APC; therefore, surgical resection was required. This patient had risk factors, such as liver cirrhosis and a history of abdominal surgery. Our case series suggests that APC is an effective hemostatic method that should be considered as the initial treatment option for radiation-induced hemorrhagic gastritis; however, surgical resection may be considered when the patient is at high risk for rebleeding.

A 44-year-old woman presented with severe anemia. We strongly suspected gastrointestinal bleeding; however, esophagogastroduodenoscopy, colonoscopy, and computed tomography showed no bleeding sources. Video capsule endoscopy revealed an actively bleeding submucosal lesion within the jejunum. Double-balloon enteroscopy revealed a 20-mm continuously bleeding submucosal lesion in the distal jejunum. We suspected small intestinal vascular malformation and performed surgical resection. The resected specimen pathologically comprised dilated, thin-walled lymphatic channels and blood vessels involving the small intestinal submucosa. Therefore, the patient was diagnosed with small intestinal lymphatic-venous malformation. Postoperatively, the patient recovered well, and recurrence was not observed.

Introduction: Endoscopic submucosal dissection (ESD) for superficial esophageal cancer is technically challenging, and research on predictive factors related to the difficulty in the procedure is limited. This study aimed to investigate the factors predicting the difficulty in esophageal ESD. Methods: This retrospective study analyzed 303 lesions treated at our institution between April 2005 and June 2021. The following 13 factors were evaluated: sex, age, tumor location, tumor localization, macroscopic type, tumor size, tumor circumference, preoperative diagnosis of histological type, preoperative diagnosis of invasion depth, previous radiotherapy for esophageal cancer, metachronous lesion located close to post ESD scar, operator’s skill, and use of a clip-and-thread traction method. Difficult esophageal ESD cases were defined as those requiring long procedure time (> 120 min). Results: Fifty-one lesions (16.8%) met the defined criterion for difficult cases of esophageal ESD. Logistic regression analysis identified tumor size larger than 30 mm (odds ratio 9.17, 95% confidence interval: 4.27–19.69, P < 0.001) and tumor circumference more than half that of the esophagus (odds ratio 2.53, 95% confidence interval: 1.15–5.54, P = 0.021) as independent predictive factors related to difficulty in esophageal ESD. Discussion/Conclusion: Tumor size larger than 30 mm and tumor circumference more than half that of the esophagus can predict difficulty in performing esophageal ESD. This knowledge can provide useful information for developing ESD strategies and selecting a suitable operator on a case-by-case basis to achieve favorable clinical outcomes.

Image recognition using artificial intelligence (AI) has progressed significantly due to innovative technologies such as machine learning and deep learning. In the field of gastric cancer (GC) management, research on AI-based diagnosis such as anatomical classification of endoscopic images, diagnosis of Helicobacter pylori infection, and detection and qualitative diagnosis of GC is being conducted, and an accuracy equivalent to that of physicians has been reported. It is expected that AI will soon be introduced in the field of endoscopic diagnosis and management of gastric cancer as a supportive tool for physicians, thus improving the quality of medical care.

Background We previously reported for the first time the usefulness of artificial intelligence (AI) systems in detecting gastric cancers. However, the “original convolutional neural network (O-CNN)” employed in the previous study had a relatively low positive predictive value (PPV). Therefore, we aimed to develop an advanced AI-based diagnostic system and evaluate its applicability for the classification of gastric cancers and gastric ulcers. Methods We constructed an “advanced CNN” (A-CNN) by adding a new training dataset (4453 gastric ulcer images from 1172 lesions) to the O-CNN, which had been trained using 13 584 gastric cancer and 373 gastric ulcer images. The diagnostic performance of the A-CNN in terms of classifying gastric cancers and ulcers was retrospectively evaluated using an independent validation dataset (739 images from 100 early gastric cancers and 720 images from 120 gastric ulcers) and compared with that of the O-CNN by estimating the overall classification accuracy. Results The sensitivity, specificity, and PPV of the A-CNN in classifying gastric cancer at the lesion level were 99.0 % (95 % confidence interval [CI] 94.6 %−100 %), 93.3 % (95 %CI 87.3 %−97.1 %), and 92.5 % (95 %CI 85.8 %−96.7 %), respectively, and for classifying gastric ulcers were 93.3 % (95 %CI 87.3 %−97.1 %), 99.0 % (95 %CI 94.6 %−100 %), and 99.1 % (95 %CI 95.2 %−100 %), respectively. At the lesion level, the overall accuracies of the O- and A-CNN for classifying gastric cancers and gastric ulcers were 45.9 % (gastric cancers 100 %, gastric ulcers 0.8 %) and 95.9 % (gastric cancers 99.0 %, gastric ulcers 93.3 %), respectively. Conclusion The newly developed AI-based diagnostic system can effectively classify gastric cancers and gastric ulcers.

Objectives Detecting early gastric cancer is difficult, and it may even be overlooked by experienced endoscopists. Recently, artificial intelligence based on deep learning through convolutional neural networks (CNNs) has enabled significant advancements in the field of gastroenterology. However, it remains unclear whether a CNN can outperform endoscopists. In this study, we evaluated whether the performance of a CNN in detecting early gastric cancer is better than that of endoscopists. Methods The CNN was constructed using 13,584 endoscopic images from 2639 lesions of gastric cancer. Subsequently, its diagnostic ability was compared to that of 67 endoscopists using an independent test dataset (2940 images from 140 cases). Results The average diagnostic time for analyzing 2940 test endoscopic images by the CNN and endoscopists were 45.5 ± 1.8 s and 173.0 ± 66.0 min, respectively. The sensitivity, specificity, and positive and negative predictive values for the CNN were 58.4%, 87.3%, 26.0%, and 96.5%, respectively. These values for the 67 endoscopists were 31.9%, 97.2%, 46.2%, and 94.9%, respectively. The CNN had a significantly higher sensitivity than the endoscopists (by 26.5%; 95% confidence interval, 14.9-32.5%). Conclusion The CNN detected more early gastric cancer cases in a shorter time than the endoscopists. The CNN needs further training to achieve higher diagnostic accuracy. However, a diagnostic support tool for gastric cancer using a CNN will be realized in the near future.

Colorectal endoscopic submucosal dissection (ESD) is technically demanding while ensuring safety, especially in cases with fibrosis and/or poor maneuverability. To overcome such difficulties, we developed a novel method called the pocket-creation method with a traction device (PCM with TD). We then evaluated the effectiveness and safety of PCM with TD in colorectal ESD compared to other conventional methods. In total, 324 colorectal lesions treated with ESD from July 2018 to June 2019 were included. The following three treatment strategies were used: conventional ESD (CE), CE with TD, and PCM with TD. Patient backgrounds and treatment outcomes were retrospectively compared and analyzed. As ESD methods, CE, CE with TD, and PCM with TD account for 58% (187/324), 24% (78/324), and 18% (59/324), respectively. No significant difference was observed among the three groups in en bloc and R0 resection rates or adverse events. The rate of lesions with fibrosis and poor maneuverability was significantly higher in the PCM with TD group (CE group vs CE with TD group vs PCM with TD group: fibrosis, 24% vs 47% vs 64%, p

This study aimed to clarify the features of superficial non-ampullary duodenal epithelial tumors (SNADETs) on magnifying endoscopy with narrow-band imaging (M-NBI) and magnifying endoscopy with acetic acid and narrow-band imaging (M-AANBI), and evaluate the efficacy of M-NBI/M-AANBI to distinguish high-grade adenomas or adenocarcinomas (HGA/AC) from low-grade adenomas (LGA).Clinicopathological data on 62 SNADETs in 58 patients who underwent preoperative M-NBI/M-AANBI and endoscopic resection were retrospectively reviewed. The pathological results were classified into two categories, LGA and HGA/AC. We evaluated microvascular patterns (MVPs) and microsurface patterns (MSPs) observed by M-NBI and MSPs observed by M-AANBI for characterizing LGA and HGA/AC. The kappa value was calculated to assess the interobserver and intraobserver agreements of evaluation of M-AANBI images.Pathologically, 38 lesions (61.3%) were LGA and 24 lesions (38.7%) were HGA/AC. HGA/AC tended to have irregular MVP and/or MSP on M-NBI. M-NBI diagnostic performance to distinguish HGA/AC from LGA showed 62.5% sensitivity, 68.4% specificity, and 66.1% accuracy. SNADETs had irregular MSP on M-AANBI. Three irregularity grades (iG) of MSP were observed by M-AANBI as follows: iG1, mild; iG2, moderate; iG3, significant. HGA/AC lesions had a significantly higher rate of iG3 than LGA lesions (p 0.001). The iG2 was associated with HGA/AC in elevated lesions and LGA in depressed lesions. The diagnostic performance of M-AANBI was as follows: 95.8% sensitivity, 97.4% specificity, and 96.8% accuracy. The diagnostic accuracy of M-AANBI was significantly higher than that of M-NBI (p 0.001). The kappa value for interobserver agreement on the diagnosis and irregularity grading of M-AANBI images was 0.742 and 0.719, respectively. These data indicate substantial interobserver agreement. Based on the above-mentioned results, we developed a M-AANBI diagnostic algorithm for SNADETs.The diagnostic algorithm for SNADETs using M-AANBI may be useful for differentiating between LGA and HGA/AC.

Background and study aims Management strategies for sporadic non-ampullary duodenal adenoma with low-grade dysplasia (LGD) are not well established. This study aimed to analyze progression factors and determine suitable treatment strategies for LGD lesions. Patients and methods We retrospectively analyzed consecutive LGD lesions (n = 125) in patients followed up for ≥ 6 months (median, 45 months) and evaluated the changes in clinicopathological features during follow-up. All LGD lesions were classified into two groups: stable LGD (no increase or Results Eighty-six LGD were classified as stable and 39 as progressive. Location on the oral side of the papilla of Vater, large initial tumor size ( ≥ 10 mm), macroscopically complex type, red color, and nodularity were significantly frequent in progressive LGD than in stable LGD. In multivariate analysis, large initial tumor size (odds ratio [OR], 10.2; 95 % confidence interval [CI], 3.3–32.1; P Conclusions According to the risk stratification of progression factors by initial tumor size and location, we can determine suitable treatment indications for LGD lesions.

Solitary colorectal metastasis of prostate cancer is very rare, but the pathological features can closely resemble a primary colorectal cancer with poorly differentiated adenocarcinoma. Thus, metastasis of prostate cancer should be considered as a differential diagnosis in male patients with colorectal lesion with poorly differentiated adenocarcinoma.

For diagnosing gastric cancer, differences in the diagnostic performance between endocytoscopy with narrow-band imaging and magnifying endoscopy with narrow-band imaging have not been reported. We aimed to clarify these differences by analyzing diagnoses made by endoscopists in Japan. This single-center retrospective cohort study used 106 cancerous and 106 non-cancerous images obtained via both modalities (total, 424 images) for diagnosis. Sixty-one endoscopists with varying experience levels from 45 institutions were included. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values were evaluated to determine the diagnostic performance of each modality and compared using the Mann–Whitney U test. Among all endoscopists, diagnostic accuracy, sensitivity, positive predictive value, and negative predictive value were higher with endocytoscopy with narrow-band imaging than with magnifying endoscopy with narrow-band imaging (percentage [95% confidence interval]: 78.8% [76.4–83.0%] versus 72.2% [69.3–73.6%], p

Colorectal endoscopic submucosal dissection (ESD) is technically demanding while ensuring safety, especially in cases with fibrosis and/or poor maneuverability. To overcome such difficulties, we developed a novel method called the pocket-creation method with a traction device (PCM with TD). We then evaluated the effectiveness and safety of PCM with TD in colorectal ESD compared to other conventional methods.In total, 324 colorectal lesions treated with ESD from July 2018 to June 2019 were included. The following three treatment strategies were used: conventional ESD (CE), CE with TD, and PCM with TD. Patient backgrounds and treatment outcomes were retrospectively compared and analyzed.As ESD methods, CE, CE with TD, and PCM with TD account for 58% (187/324), 24% (78/324), and 18% (59/324), respectively. No significant difference was observed among the three groups in en bloc and R0 resection rates or adverse events. The rate of lesions with fibrosis and poor maneuverability was significantly higher in the PCM with TD group (CE group vs CE with TD group vs PCM with TD group: fibrosis, 24% vs 47% vs 64%, p 0.001; poor maneuverability, 5.3% vs 13% vs 20%, p = 0.002). Dissection speed was significantly higher in the PCM with TD than in the CE with TD group (p = 0.003).PCM with TD can achieve a stable en bloc resection rate and R0 dissection rate without adverse events even in the hands of trainees, irrespective of the size and location of the lesion, presence of fibrosis, and under poor maneuverability conditions.

Image recognition using artificial intelligence(AI)has developed dramatically with innovative technologies such as machine learning and deep learning. Currently, it is considered that AI has exceeded human ability in image recognition. In the field of endoscopic diagnosis, development of computer-aided diagnosis(CAD)systems using AI is progressing. The CAD is expected to help endoscopists improve detection and characterization of polyp, cancer, and inflamation in all digestive area. Some CAD systemes showing ability better than endoscopists have been reported. It may be well applicable to daily clinical practice as real time endoscopic diagnosis in the near future.